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1
Employing Agent-based Models to study Interdomain Network Formation,
Dynamics & Economics
Aemen Lodhi (Georgia Tech)
Workshop on Internet Topology & Economics (WITE’12)
2
Outline
• Agent-based modeling for AS-level Internet
• Our model: GENESIS• Application of GENESIS– Large-scale adoption of Open peering
strategy
• Conclusion
3
What is the environment that we are we trying to model?
• Autonomous System level Internet• Economic network
Enterprise customer
Transit Provider
Transit Provider
Internet
Enterprise customer
Content Provider
Content Provider
4
What is the environment that we are we trying to model?
• Complex, dynamic environment– Mergers, acquisitions, new entrants, bankruptcies– Changing prices, traffic matrix, geographic
expansion
• Co-evolutionary network• Self-organization• Information “fuzziness”• Social aspects: 99% of all peering
relationships are “handshake” agreements*
*”Survey of Characteristics of Internet Carrier Interconnection Agreements 2011” – Packet Clearing House
5
What are we asking?
• Aggregate behavior– Is the network stable?– Is their gravitation towards a particular
behavior e.g., Open peering– Is their competition in the market?
• Not so academic questions– Is this the right peering strategy for me?–What if I depeer AS X?– Should I establish presence at IXP Y?– CDN: Where should I place my caches?
6
Different approaches
• Analytical / Game-theoretic approach• Empirical studies• Generative models e.g., Preferential
attachment• Distributed optimization• Agent-based modeling
7
Why to use agent-based modeling?
• Incorporation of real-world constraints– Non-uniform traffic matrix– Complex geographic co-location patterns–Multiple dynamic prices per AS– Different peering strategies at different
locations• Scale – hundreds of agents• What-if scenarios• Understanding the “process” and not just
the “end-state”
8
Why not to use agent-based modeling?
• Large parameterization space– Systematic investigation of full
parameter space is difficult
• Validation• Computational cost• Under some circumstances reasoning
may be difficult e.g. instability in a model with hundreds of agents
10
The model: GENESIS*
• Agent based interdomain network formation model
• Fundamental unit: An agent (AS) with economic interests
• Incorporates– Co-location constraints in provider/peer
selection– Traffic matrix– Public & Private peering– Set of peering strategies– Peering costs, Transit costs, Transit revenue
*Aemen Lodhi, Amogh Dhamdhere, Constantine Dovrolis, “GENESIS: An agent-based model of interdomain network formation, traffic flow and economics,” InfoCom 2012
11
Geographic presence & constraints
Link formation
across geography
not possible
Regions corresponding to unique
IXPs
Peering link at top tier
possible across regions
Geographic overlap
The model: GENESIS*
Fitness = Transit Revenue – Transit Cost – Peering cost
• Objective: Maximize economic fitness• Optimize connectivity through peer
and transit provider selection• Choose the peering strategy that
maximizes fitness
13
Peering strategies
• Restrictive: Peer only to avoid network partitioning
• Selective: Peer with ASes of similar size
• Open: Every co-located AS except customers
• Choose peering strategy that is predicted to give maximum fitness
14
Peering strategy adoption
• No coordination, limited foresight• Eventual fitness can be different • Stubs always use Open peering
strategy
Time
1 2 3
Depeering Peering Transit Provider selection
Open Selective Open
15
Application of GENESIS:Analysis of peering strategy
adoption by transit providers in the Internet*
*Aemen Lodhi, Amogh Dhamdhere, Constantine Dovrolis, “Analysis of peering strategy adoption by transit providers in the Internet,” NetEcon 2012
16
Motivation: Existing peering environment
• Increasing fraction of interdomain traffic flows over peering links*
• How are transit providers responding?
Transit Provider
Content Provider/CD
N
Access ISP/Eyeballs
*C. Labovitz, S. Iekel Johnson, D. McPherson, J. Oberheide and F. Jahanian, “Internet Interdomain Traffic,” in ACM SIGCOMM, 2010
17
Motivation: Existing peering environment
• Peering strategies of ASes in the Internet (source: PeeringDB www.peeringdb.com)• Transit Providers peering openly ?
Approach
• Agent based computational modeling• Corroboration by PeeringDB data• Scenarios
*Stubs always use Open
Without-open• Selective • Restrictive
With-open• Selective• Restrictive• Open
vs.
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Strategy adoption by transit providers
Conservative Non-conservative0
10
20
30
40
50
60
70
80
90
100
RestrictiveSelectiveOpen
Scenarios
Perc
en
tag
e o
f tr
an
sit
pro
vid
ers
Without-open With -open
20
Collective impact of Open peering on fitness of transit providers
• Cumulative fitness reduced in all simulations
21
Impact on fitness of individual transit providers switching from Selective to Open
• 70% providers have their fitness reduced
Why do transit providers adopt Open peering?
x y
z w
vSave transit
costs
But your customers are
doing the same!
Affects:• Transit Cost
• Transit Revenue• Peering Cost
Why gravitate towards Open peering?
x y
z wz w,z y,traffic bypasses x
x lost transit revenu
e
Options for x?
x regains lost transit
revenue partially
Y peering openly
x adopts Open peering
Not isolated decisionsNetwork effects !!
z w,traffic passes through x again!
24
• Employ agent-based models for large-scale study of interdomain network formation
• Parameterization and validation are difficult
• Agent-based models can reveal surprising behavior
Conclusion
25
• Gravitation towards Open peering is a network effect for transit providers (70% adopt Open peering)– Economically motivated strategy
selection–Myopic decisions– Lack of coordination
• Extensive Open peering by transit providers in the network results in collective loss
Conclusion